McVeigh noted that Intel’s integrated accelerators will be complemented by the upcoming discrete GPUs. He called the Flex Series GPUs “HPC on the edge,” with their low power envelopes, and pointed to Ponte Vecchio – complete with 100 billion transistors in 47 chiplets that leverage both Intel 7 manufacturing processes as well as 5 nanometer and 7 nanometer processes from Taiwan Semiconductor Manufacturing Co – and then Rialto Bridge. Both Ponte Vecchio and Sapphire Rapids will be key components in Argonne National Labs’ Aurora exascale supercomputer, which is due to power on later this year and will deliver more than 2 exaflops of peak performance. .... “Another part of the value of the brand here is around the software unification across Xeon, where we leverage the massive amount of capabilities that are already established through decades throughout that ecosystem and bring that forward onto our GPU rapidly with oneAPI, really allowing for both the sharing of workloads across CPU and GPU effectively and to ramp the codes onto the GPU faster than if we were starting from scratch,” he said.
Our multi-tenant Postgres instances operate on bare metal servers in non-containerized environments. Each backend application service is considered a single tenant, where they may use one of multiple Postgres roles. Due to each cluster serving multiple tenants, all tenants share and contend for available system resources such as CPU time, memory, disk IO on each cluster machine, as well as finite database resources such as server-side Postgres connections and table locks. Each tenant has a unique workload that varies in system level resource consumption, making it impossible to enforce throttling using a global value. This has become problematic in production affecting neighboring tenants:Throughput. A tenant may issue a burst of transactions, starving shared resources from other tenants and degrading their performance. Latency: A single tenant may issue very long or expensive queries, often concurrently, such as large table scans for ETL extraction or queries with lengthy table locks. Both of these scenarios can result in degraded query execution for neighboring tenants. Their transactions may hang or take significantly longer to execute due to either reduced CPU share time, or slower disk IO operations due to many seeks from misbehaving tenant(s).
Quantum will enable enterprise customers to perform complex simulations in significantly less time than traditional software using quantum computers. Quantum algorithms are very challenging to develop, implement, and test on current Quantum computers. Quantum techniques also are being used to improve the randomness of computer-based random number generators. The world’s leading quantum scientists in the field of quantum information engineering, working to turn what was once in the realm of science fiction. Businesses need to deploy next-generation data security solutions with equally powerful protection based on the laws of quantum physics, literally fighting quantum computers with quantum encryption Quantum computers today are no longer considered to be science fiction. The main difference is that quantum encryption uses quantum bits or qubits comprised of optical photons compared to electrical binary digits or bits. Qubits can also be inextricably linked together using a phenomenon called quantum entanglement.
We are constantly exposed to and engaged with various visually similar objects around us. By using machine learning techniques, the discipline of AI known as computer vision enables machines to see, comprehend, and interpret the visual environment around us. It uses machine learning approaches to extract useful information from digital photos, movies, or other observable inputs by identifying patterns. Although they have the same appearance and sensation, they differ in a few ways. Computer vision aims to distinguish between, classify, and arrange images according to their distinguishing characteristics, such as size, color, etc. This is similar to how people perceive and interpret images. ... Digital image processing uses a digital computer to process digital and optical images. A computer views an image as a two-dimensional signal composed of pixels arranged in rows and columns. A digital image comprises a finite number of elements, each located in a specific place with a particular value. These so-called elements are also known as pixels, visual, and image elements.
In the decades since the movie’s release, the world has become a different place in some important ways. Women are now everywhere in the world of business, which has changed irrevocably as a result. Unemployment is quite low in the United States and, by Continental standards, in Europe. Recent downturns have been greeted by large-scale stimuli from central banks, which have blunted the impact of stock market slides and even a pandemic. But it would be foolish to think that the horrendous managers and desperate salesmen of Glengarry Glen Ross exist only as historical artifacts. Mismanagement and desperation go hand in hand and are most apparent during hard times, which always come around sooner or later. By immersing us in the commercial and workplace culture of the past, movies such as Glengarry can help us understand our own business culture. But they can also help prepare us for hard times to come—and remind us how not to manage, no matter what the circumstances. ... Everyone, in every organization, has to perform.
As energy sector organisations continue expanding their connectivity to improve efficiency, they must ensure that the perimeters of their security processes keep up. Without properly secured infrastructure, no digital transformation will ever be successful, and not only internal operations, but also the data of energy users are bound to become vulnerable. But by following the above recommendations, energy companies can go a long way in keeping their infrastructure protected in the long run. This endeavour can be strengthened further by partnering with cyber security specialists like Dragos, which provides an all-in-one platform that enables real-time visualisation, protection and response against ever present threats to the organisation. These capabilities, combined with threat intelligence insights and supporting services across the industrial control system (ICS) journey, is sure to provide peace of mind and added confidence in the organisation’s security strategy. For more information on Dragos’s research around cyber threat activity targeting the European energy sector, download the Dragos European Industrial Infrastructure Cyber Threat Perspective report, here.
The global pandemic has forever changed the way we work. The remote work model has been successful, and we’ve learned that productivity does not necessarily decrease when managers and their team members are not physically together. This has been a boon for Gen Z – a generation that grew up surrounded by technology. Creating an environment that gives IT employees the flexibility to conduct their work remotely has opened the door to a truly global workforce. Combined with the advances in digital technologies, we’ve seen a rapid and seamless transition in how employment is viewed. Digital transformation has leveled the playing field for many companies by changing requirements around where employees need to work. Innovative new technologies, from videoconferencing to IoT, have shifted the focus from an employee’s location to their ability. Because accessing information and managing vast computer networks can be done remotely, the location of workers has become a minor issue.
Enterprise security teams, which over the years have honed their ability to detect the use of Cobalt Strike by adversaries, may also want to keep an eye out for "Sliver." It's an open source command-and-control (C2) framework that adversaries have increasingly begun integrating into their attack chains. "What we think is driving the trend is increased knowledge of Sliver within offensive security communities, coupled with the massive focus on Cobalt Strike [by defenders]," says Josh Hopkins, research lead at Team Cymru. "Defenders are now having more and more successes in detecting and mitigating against Cobalt Strike. So, the transition away from Cobalt Strike to frameworks like Sliver is to be expected," he says. Security researchers from Microsoft this week warned about observing nation-state actors, ransomware and extortion groups, and other threat actors using Sliver along with — or often as a replacement for — Cobalt Strike in various campaigns. Among them is DEV-0237, a financially motivated threat actor associated with the Ryuk, Conti, and Hive ransomware families; and several groups engaged in human-operated ransomware attacks, Microsoft said.
When your data is spread across multiple clouds and systems, it can introduce latency, performance, and quality problems. And bringing together data from different silos and getting those data sets to speak the same language is a time- and budget-intensive endeavor. Your existing data platforms also may prevent you from managing hybrid data processing, which, as Ventana Research explains, “enable[s] analysis of data in an operational data platform without impacting operational application performance or requiring data to be extracted to an external analytic data platform.” The firm adds that: “Hybrid data processing functionality is becoming increasingly attractive to aid the development of intelligent applications infused with personalization and artificial intelligence-driven recommendations.” Such applications are clearly important because they can be key business differentiators and enable you to disrupt a sector. However, if you are grappling with siloed systems and data and legacy technology that is unable to ingest high volumes of complex data fast so that you can act in the moment, you may believe that it is impossible for your business to benefit from the data synergies that you and your customers might otherwise enjoy.
Everybody knows data quality is essential. Most companies spend significant money and resources trying to improve data quality. However, despite these investments, companies lose money yearly because of insufficient data, ranging from $9.7 million to $14.2 million annually. Traditional data quality programs do not work well for identifying data errors in cloud environments because:Most organizations only look at the data risks they know, which is likely only the tip of an iceberg. Usually, data quality programs focus on completeness, integrity, duplicates and range checks. However, these checks only represent 30 to 40 percent of all data risks. Many data quality teams do not check for data drift, anomalies or inconsistencies across sources, contributing to over 50 percent of data risks. The number of data sources, processes and applications has exploded because of the rapid adoption of cloud technology, big data applications and analytics. These data assets and processes require careful data quality control to prevent errors in downstream processes. The data engineering team can add hundreds of new data assets to the system in a short period.
Quote for the day:
"Problem-solving leaders have one thing in common: a faith that there's always a better way." -- Gerald M. Weinberg